The synergy of Sentinel missions for fire damage assessment on land surface and atmosphere: the Arakapas village case study

Fires are a widespread ecological factor since ancient times. It has a negative impact not only on the environment but on the economy, society and people. A forest fire can lead to a change in land surface, the destruction of large areas of vegetation and soil erosion. As a result, the economy is negatively affected, the balance of ecosystems is disturbed, and the flora, fauna and natural beauty are destructed. Also, biomass burning smoke affects air quality due to the large quantities of trace gases and aerosol particles that are emitted, leading to global climate change and playing a significant role in troposphere chemistry. A fundamental tool for forest fire management is the science of remote sensing. Remote sensing is commonly used for mapping burnt areas as well as for studying the effects of fire incidents and this statement is very well supported by the literature at local, regional and global levels. This study is mainly focused on burned area mapping and damage assessment on land surface and atmosphere for the case of the Arakapas fire in Cyprus. For the purposes of this study, the satellite images acquired from Sentinel-2 were used for the burnt area mapping and the fire severity estimation based on the dNBR (difference Normalized Burn Ratio) spectral index, and the Corine land cover was used for the assessment of the vegetation type that was disturbed. This event considered one of the largest in recent years is explored using data from Sentinel-5P, where carbon monoxide product is studied in the region affected by the fires. Furthermore, on the morning of the 5th of July, due to the change of wind direction, the smoke travelled from the centre of the island to the southwest, and it was detected by the multiwavelength Raman lidar, installed in Limassol. Thus, the optical properties of the smoke plume retrieved from the lidar are presented. The PollyXT-CYP lidar system of the ECoE, observed multiple layers between 500m and 2.5km with depolarization ratio of 5-8% and lidar ratio of 75sr for the upper layers.For the purposes of this study, the image processing was performed using custom scripts in the GEE (Google Earth Engine) platform with the JavaScript programming interface. The area affected by the fire was calculated to be ~40Km². The spatial distribution map of the dNBR was classified according to the USGS fire severity levels, where high dNBR values indicate a more severe fire and values near zero and negative values indicate unburned and/or decreased vegetation after the fire.

 

Acknowledgements

The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.